Weather radars are capable of detecting and displaying storm-related turbulence as well as precipitation rate with fine spatial resolution and reliable quality. Especially, weather radars can enable the detection of atmospheric conditions in the vicinity of cities and thereby help to notify the strong winds. This article investigates the application of weather radar measurements for predicting strong wind and presents a new method for very short-term storm prediction. In the proposed method, hidden Markov model (HMM) is used to classify atmospheric conditions to "potentially stormy" and "non-stormy" states using the available radar data, and semi-Markov theory is used to estimate the probability of storm occurrence with time. In fact, the probability of transition between "potentially stormy," "non-stormy," and "stormy" states is modeled by a semi-Markov model, to find the unconditional probability of storm occurrence with time. The model is implemented with the use of the data of Tehran C-band weather radar and anemometer of Tehran international airport.Verification results show that the precision (forecast accuracy) is around 0.19 and the recall is around 0.67 in the presented classification method.

The wind is one of the most important and affecting phenomena and is known as one of the significant clean resources of energy. Apart from other atmospheric parameters, the wind has complex behavior and intermittent characteristics. Local phenomena can be accompanied by the wind, which is strong, non-predicted, and damaging. Weather radars are capable of detecting and displaying storm-related turbulence as well as precipitation in a relatively wide area. This capability can improve the quality of the wind forecast. In this paper, a method is presented and implemented to forecast the probability of strong wind in the next five hours based on the Hidden Markov Model (HMM). The method is expanded to find out the forecast of wind turbine output power and reliability as well. Achieved results show that about 67% of strong winds are correctly forecasted.

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